Retail and publishing are both – by and large – low margin businesses. Few companies in either vertical can survive without volume — and more recently many are realizing that it’s a small cohort of loyal customers that drive the highest volume of profits.

Those businesses relying purely on acquisition to maintain have tragedy looming. But there’s a new force on the scene that is turning the potential doom into a story with a bit more light.

Enter machine learning.

Harvard Business Review recently studied 168 early adopters of machine-reengineering. These are the organizations leading the way in their applied use of machine learning to establish new forms of human-machine collaboration. The results from these early use-cases include operational speed and efficiencies, both of which promote breaking through bottlenecks of complex processes.

Machine learning is at the core of what makes Sailthru unique. Take our work with The Clymb, a leading online adventure and gear retailer. Before Sailthru, The Clymb struggled with siloed data sets, disparate marketing efforts and a lack of connection to their individual customers. These are traits all-too-common for so-called modern retailers. With Sailthru, the company not only modernized, but they level-jumped from a batch and blast approach to customer engagement, to delivering fully individualized experiences based on a combination of buyer interests and intent. They know which customers will purchase, which will open email and those likely to opt-out. The intelligence is automatically applied to marketing decisions: those who will purchase get served with the right messages to drive conversion, those not likely to open email receive communications via other channels, and those likely to opt-out get suppressed from sends.

Similarly on the publishing side; we’re helping brands like Groupe Editialis, a Paris-based publishing enterprise, predict which individuals will drive high volumes of pageviews, in addition to a long list of other behaviors. Their marketers not only know who to message, but how many pageviews they can expect from their known audience to support ad-revenue forecasting.

HBR covers two primary applications for machine-reengineering in this piece on how companies are using machine learning to get faster and more efficient:

“It’s already clear that machine-reengineering has the power to help manage the data deluge — and resulting bottlenecks — that modern organizations face. Workers can become more efficient and effective, which improves workflows as well as the bottom line. If data is the path forward, machine-reengineering is paving the way.”

Read more at HBR to find new ways that machine learning can change your trajectory.